demo.py

#!/usr/bin/env python3
"""
Quick demo script to test Mamba Graph implementation
Device-safe version
"""

import torch
import os
from core.graph_mamba import GraphMamba
from data.loader import GraphDataLoader
from utils.metrics import GraphMetrics

def main():
    print("🧠 Testing Mamba Graph Neural Network")
    print("=" * 50)
    
    # Configuration
    config = {
        'model': {
            'd_model': 128,
            'd_state': 8,
            'd_conv': 4,
            'expand': 2,
            'n_layers': 3,
            'dropout': 0.1
        },
        'data': {
            'batch_size': 16,
            'test_split': 0.2
        },
        'ordering': {
            'strategy': 'bfs',
            'preserve_locality': True
        }
    }
    
    # Setup device
    if os.getenv('SPACE_ID'):
        device = torch.device('cpu')
    else:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print(f"Device: {device}")
    
    # Load dataset
    print("\n📊 Loading Cora dataset...")
    try:
        data_loader = GraphDataLoader()
        dataset = data_loader.load_node_classification_data('Cora')
        data = dataset[0].to(device)
        
        # Dataset info
        info = data_loader.get_dataset_info(dataset)
        print(f"✅ Success!")
        print(f"Nodes: {data.num_nodes}")
        print(f"Edges: {data.num_edges}")
        print(f"Features: {info['num_features']}")
        print(f"Classes: {info['num_classes']}")
        
    except Exception as e:
        print(f"❌ Error loading dataset: {e}")
        return
    
    # Initialize model
    print("\n🏗️ Initializing GraphMamba...")
    try:
        model = GraphMamba(config).to(device)
        total_params = sum(p.numel() for p in model.parameters())
        print(f"✅ Model initialized!")
        print(f"Parameters: {total_params:,}")
        
    except Exception as e:
        print(f"❌ Error initializing model: {e}")
        return
    
    # Forward pass test
    print("\n🚀 Testing forward pass...")
    try:
        model.eval()
        with torch.no_grad():
            h = model(data.x, data.edge_index)
            print(f"✅ Forward pass successful!")
            print(f"Input shape: {data.x.shape}")
            print(f"Output shape: {h.shape}")
            print(f"Output range: [{h.min():.3f}, {h.max():.3f}]")
            
    except Exception as e:
        print(f"❌ Forward pass failed: {e}")
        return
    
    # Test different ordering strategies
    print("\n🔄 Testing ordering strategies...")
    
    strategies = ['bfs', 'spectral', 'degree', 'community']
    
    for strategy in strategies:
        try:
            config['ordering']['strategy'] = strategy
            model_test = GraphMamba(config).to(device)
            model_test.eval()
            
            with torch.no_grad():
                h = model_test(data.x, data.edge_index)
            print(f"✅ {strategy}: Success - Shape {h.shape}")
            
        except Exception as e:
            print(f"❌ {strategy}: Failed - {str(e)}")
    
    # Test evaluation
    print("\n📈 Testing evaluation...")
    try:
        # Initialize classifier
        num_classes = info['num_classes']
        model._init_classifier(num_classes, device)
        
        # Create test mask if not available
        if hasattr(data, 'test_mask'):
            mask = data.test_mask
        else:
            mask = torch.zeros(data.num_nodes, dtype=torch.bool, device=device)
            mask[data.num_nodes//2:] = True
        
        metrics = GraphMetrics.evaluate_node_classification(model, data, mask, device)
        print("✅ Evaluation successful!")
        for metric, value in metrics.items():
            if isinstance(value, float):
                print(f"  {metric}: {value:.4f}")
            
    except Exception as e:
        print(f"❌ Evaluation failed: {e}")
    
    print("\n✨ Demo completed!")
    print("🚀 Ready for production deployment!")

if __name__ == "__main__":
    main()